Mixture model(s) are used, for example, to build and/or improve data mining models for density, regression and/or classification model(s). Statisticians and computer scientists have used these models for statistical inference or learning from data. In particular, model selection and model averaging techniques have been applied to models for the purposes of prediction and identifying cause and effect from observational data. The basic idea behind these endeavors has been that many domains exhibit conditional independence (e.g., due to causal relationships) and mixture models are useful for capturing these relationships.